This research project aims to develop a computer model for the detection of areas at high risk for becoming foci for Lyme disease. Solving this type of problem is typically attacked by applying the techniques of statistical analysis to empirical data. The investigator's approach is to implement fuzzy logic inferencing procedures as computer software so that these techniques can be applied to a public health problem for which the relationships between vectors and their habitats are not yet clearly known. The computer model uses fuzzy logic to make inferences from a rule base. Rules are constructed regarding relationships between variables which are described by adjectives. Adjectives can be modified by adverbs, and complex rules can be formed through conjunction. Adjectives are described graphically. The proposed computer model will require a minimal set of information about a habitat's characteristics, such as the types and extent of covering vegetation, the local climate, and the pool of hosts and reservoirs of lxodes dammini, the tick vector of Borrelia burgdorferi, the etiological agent of Lyme borreliosis. From these data the fuzzy logic inference algorithm will assess the likelihood that the specified area can support the development of Lyme disease foci.